Electricity theft, particularly through line hooking, poses a significant challenge in regions with less advanced electricity infrastructure. Traditional detection methods are often ineffective due to outdated infrastructure and manual enforcement approaches. This study proposes an IoT-based model integrating proximity sensors, object detection algorithms, and edge computing to enhance real-time detection and reporting of line hooking. Using a three-phase design incorporating Agent-Based Modelling, Discrete Event Simulation, and Tiny Machine Learning, the model ensures rapid detection, reduces false alarms, and enables autonomous theft prevention. Preliminary results demonstrate the model’s potential to improve electricity theft detection, with future work focusing on implementation and evaluation in real-world scenarios. The proposed solution minimizes reliance on stable network connectivity by processing data locally using edge computing. This approach enhances the model’s resilience and ensures continuous operation in areas with unreliable communication infrastructure. By addressing a critical gap in existing solutions, this research contributes to more effective electricity theft prevention strategies tailored for developing countries.